Markov chain Monte Carlo samplers. More...

Classes
class	adapt_dense_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More...

class	adapt_dense_e_nuts_classic

class	adapt_dense_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and adative dense metric and adaptive step size. More...

class	adapt_dense_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More...

class	adapt_dense_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive dense metric and adaptive step size. More...

class	adapt_diag_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More...

class	adapt_diag_e_nuts_classic

class	adapt_diag_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More...

class	adapt_diag_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More...

class	adapt_diag_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and adaptive diagonal metric and adaptive step size. More...

class	adapt_softabs_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More...

class	adapt_softabs_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More...

class	adapt_softabs_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More...

class	adapt_softabs_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric and adaptive step size. More...

class	adapt_unit_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More...

class	adapt_unit_e_nuts_classic

class	adapt_unit_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More...

class	adapt_unit_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More...

class	adapt_unit_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric and adaptive step size. More...

class	base_adaptation

class	base_adapter

class	base_hamiltonian

class	base_hmc

class	base_integrator

class	base_leapfrog

class	base_mcmc

class	base_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling. More...

class	base_nuts_classic

class	base_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time. More...

class	base_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time. More...

class	base_xhmc
	Exhaustive Hamiltonian Monte Carlo (XHMC) with multinomial sampling. More...

class	chains
	An `mcmc::chains` object stores parameter names and dimensionalities along with samples from multiple chains. More...

class	covar_adaptation

class	dense_e_metric

class	dense_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and dense metric. More...

class	dense_e_nuts_classic

class	dense_e_point
	Point in a phase space with a base Euclidean manifold with dense metric. More...

class	dense_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and dense metric. More...

class	dense_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and dense metric. More...

class	dense_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and dense metric. More...

class	diag_e_metric

class	diag_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and diagonal metric. More...

class	diag_e_nuts_classic

class	diag_e_point
	Point in a phase space with a base Euclidean manifold with diagonal metric. More...

class	diag_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and diagonal metric. More...

class	diag_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and diagonal metric. More...

class	diag_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and diagonal metric. More...

class	expl_leapfrog

class	fixed_param_sampler

class	impl_leapfrog

struct	nuts_util

class	ps_point
	Point in a generic phase space. More...

class	sample

struct	softabs_fun

class	softabs_metric

class	softabs_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric. More...

class	softabs_point
	Point in a phase space with a base Riemannian manifold with SoftAbs metric. More...

class	softabs_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric. More...

class	softabs_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Riemannian disintegration and SoftAbs metric. More...

class	softabs_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Riemannian disintegration and SoftAbs metric. More...

class	stepsize_adaptation

class	stepsize_adapter

class	stepsize_covar_adapter

class	stepsize_var_adapter

class	unit_e_metric

class	unit_e_nuts
	The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric. More...

class	unit_e_nuts_classic

class	unit_e_point
	Point in a phase space with a base Euclidean manifold with unit metric. More...

class	unit_e_static_hmc
	Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric. More...

class	unit_e_static_uniform
	Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integration time with a Gaussian-Euclidean disintegration and unit metric. More...

class	unit_e_xhmc
	Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disintegration and unit metric. More...

class	var_adaptation

class	windowed_adaptation

Functions
void	write_metric (stan::interface_callbacks::writer::base_writer &writer)

void	stable_sum (double a1, double log_w1, double a2, double log_w2, double &sum_a, double &log_sum_w)
	a1 and a2 are running averages of the form $a1 = ( \sum_{n \in N1} w_{n} f_{n} ) / ( \sum_{n \in N1} w_{n} )$ $a2 = ( \sum_{n \in N2} w_{n} f_{n} ) / ( \sum_{n \in N2} w_{n} )$ and the weights are the respective normalizing constants $w1 = \sum_{n \in N1} w_{n}$ $w2 = \sum_{n \in N2} w_{n}.$ More...

Detailed Description

Markov chain Monte Carlo samplers.

Function Documentation

§ stable_sum()

void stan::mcmc::stable_sum	(	double	a1,
		double	log_w1,
		double	a2,
		double	log_w2,
		double &	sum_a,
		double &	log_sum_w
	)

a1 and a2 are running averages of the form $a1 = ( \sum_{n \in N1} w_{n} f_{n} ) / ( \sum_{n \in N1} w_{n} )$ $a2 = ( \sum_{n \in N2} w_{n} f_{n} ) / ( \sum_{n \in N2} w_{n} )$ and the weights are the respective normalizing constants $w1 = \sum_{n \in N1} w_{n}$ $w2 = \sum_{n \in N2} w_{n}.$

This function returns the pooled average $sum_a = ( \sum_{n \in N1 \cup N2} w_{n} f_{n} ) / ( \sum_{n \in N1 \cup N2} w_{n} )$ and the pooled weights $ log_sum_w = log(w1 + w2). $

Parameters

a1	First running average, f1 / w1
log_w1	Log of first summed weight
a2	Second running average
log_w2	Log of second summed weight
sum_a	Average of input running averages
log_sum_w	Log of summed input weights

Definition at line 39 of file base_xhmc.hpp.

§ write_metric()

void stan::mcmc::write_metric ( stan::interface_callbacks::writer::base_writer & writer )

Definition at line 18 of file unit_e_point.hpp.

Classes

Functions

Detailed Description

Function Documentation

§ stable_sum()

§ write_metric()